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| Content Provider | IEEE Xplore Digital Library | 
|---|---|
| Author | Xiaoyong Ye Yong Lei Haijun Hou | 
| Copyright Year | 2008 | 
| Description | Author affiliation: Sch. of Electr. Eng. & Inf., Sichuan Univ., Chengdu (Xiaoyong Ye) | 
| Abstract | The intelligent mobile vehicle (IMV) is designed on embedded system with ARM9 and Windows CE.NET embedded operation system (EOS). It is a completely autonomous mobile vehicle system with hardware executive mechanics and corresponding intelligent path planning algorithm. The goal for the IMV is to run from the start point to the end point along the shortest path in any random scene autonomously. There are three problems needed to be solved to fulfill the goal: acquire the scene information, quickly plan out the global shortest path and track the optimal path. The global scene information is got through camera on top, then processed and transmitted to the IMV by radio and the global optimal path is planned out based on improved ant colony algorithm. The IMV tracks the optimal path by controlling the driver wheels with two DC motors based on fuzzy control method. Many sensors also must be equipped to assist tracking the shortest path. All the algorithms are realized with C++ under Windows CE.NET. The experiments show that the IMV system has a good performance in planning and tracking the global optimal path autonomously. | 
| Starting Page | 1 | 
| Ending Page | 5 | 
| File Size | 126945 | 
| Page Count | 5 | 
| File Format | |
| ISBN | 9781424417056 | 
| DOI | 10.1109/ICIT.2008.4608359 | 
| Language | English | 
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) | 
| Publisher Date | 2008-04-21 | 
| Publisher Place | China | 
| Access Restriction | Subscribed | 
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) | 
| Subject Keyword | Path planning Hardware Cameras Algorithm design and analysis Vehicles Sensors Mobile robots path planning ant colony algorithm embedded operation system intelligent mobile vehicle | 
| Content Type | Text | 
| Resource Type | Article | 
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